1. Field of the Invention
The present invention relates to a novel method for intelligent data acquisition for a measurement system. The method has particular applicability in intelligent data acquisition from a system which can be used to monitor a semiconductor processing tool.
2. Description of the Related Art
Large volumes of data tend to be generated in on-line measurement systems, leading to significant difficulties in data handling and data acquisition. For example, in the field of in-situ particle monitoring, used widely in the semiconductor manufacturing industry, a particle counter is often incorporated into or added to the exhaust line of a semiconductor processing tool. Particles in the exhaust line are then counted continuously, and the measured particle counts are used as an indicator of the quality of the semiconductor manufacturing process. Full utilization of the particle measurements, however, is often hampered by the sheer volume of data accumulated. This is compounded by the difficulty in correlating particle counts in the exhaust line with defects on the product wafers.
In-situ monitoring of moisture in the semiconductor manufacturing industry is a relatively new field. This method is of particular interest since moisture is generally considered a more useful indicator of the extent of contamination in a semiconductor processing tool than are particle monitoring systems. Although in-situ moisture monitoring is a much newer field than in-situ particle monitoring, field trials have revealed that the problems associated with the handling of large quantities of data in particle monitoring are also encountered with moisture monitoring.
The concentration of the gas species being measured can be calculated based on the results of the absorption measurement together with the sample pressure, the sample temperature, the length of the diode laser light path and the nature of the gas species present. This calculation is well known, and is described, for example, by R. D. May and C. R. Webster, Journal of Quantitative Spectroscopy and Radiative Transfer, Vol. 49(4), pp. 335-347 (1993).
Data collection from a diode laser system may be automated as described by C. R. Webster et al, Applied Optics Vol. 33(3), pp. 454-472 (1994). Although this publication and the previously mentioned publication are concerned with airborne measurement of atmospheric components, the same electronics and data processing can be applied to in-situ moisture monitoring. The primary difference between the two methods lies in the timing of the calculations following measurement. In the airborne measurement of atmospheric components, multiple spectra are collected during flight followed by moisture concentration determination. Because it is important that the data be more immediately available for in-situ moisture monitoring, it is necessary that moisture concentrations be calculated directly after the measurement of each spectrum.
The output from the in-situ moisture sensor is a record of moisture concentration versus time, with the interval between moisture concentration measurements typically being in the range of from about one to three seconds. Because the in-situ sensor is often operated unattended for days or weeks at a time, a large volume of data can rapidly accumulate over that time period.
In a typical data collection system, moisture concentration and several other diagnostic parameters are written to a file on a memory storage device, called a "data file." At predefined intervals of, for example, 20 minutes, the currently open data file is saved and closed, and a new data file is opened. Although the period corresponding to data collection for a given data file can be longer, this period is generally set at less than one hour due to limitations of memory encountered and the risk of losing large blocks of data in the event a data file is corrupted. The data file is assigned a name which is generated automatically, for example, according to the date and time at which it is saved.
According to a procedure currently in use in many data acquisition systems, and in particular, in unattended diode laser systems, a signal to save and close the currently open data file and to open a new data file is sent to the measurement system after a counter reaches some predefined number. This counter is incremented by one each time a new moisture concentration is calculated and the corresponding record is written to the data file.
In these continuously operating measurement systems, it is necessary to examine all of the data collected for proper analysis of the process which is being monitored. Although various aspects of the data collected can be readily automated (e.g., by data charting), the data review task remains very time consuming.
In addition, depending upon the utilization of the processing tool with which the moisture sensor is associated, the importance of different blocks of data collected can vary widely. A known solution to the above-described problem, used in the field of in-situ particle counting, is to provide a trigger to the measurement system whenever the processing tool becomes active. The measurement system then operates only after becoming activated by the trigger, i.e., during actual operation of the processing tool. One disadvantage of this solution is the inability to collect data when the processing tool is inactive, thereby preventing the collection of baseline data. Moreover, a compatible output from the processing tool is required to provide the measurement system with such a trigger. Because compatible outputs are sometimes lacking, a trigger is often unavailable. Finally, this solution does not reduce the total amount of data collected during processing, nor does it do anything to increase utilization of that data.
To overcome the disadvantages of the prior art, it is an object of the present invention to provide a novel method for intelligent data acquisition in a measurement system, which method can substantially reduce the size of the data storage system called for, as well as the time required for review of the collected data. The inventive method further eliminates the need for a compatible output signal from a semiconductor processing tool.